Book Image

Machine Learning with BigQuery ML

By : Alessandro Marrandino
Book Image

Machine Learning with BigQuery ML

By: Alessandro Marrandino

Overview of this book

BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.
Table of Contents (20 chapters)
Section 1: Introduction and Environment Setup
Section 2: Deep Learning Networks
Section 3: Advanced Models with BigQuery ML
Section 4: Further Extending Your ML Capabilities with GCP

Chapter 9: Suggesting the Right Product by Using Matrix Factorization

Suggesting the right product is one of the most common applications of Machine Learning (ML). Every day, product recommendation systems influence our choices on the internet. Newsletters, e-commerce websites, video streaming companies, and many other services leverage this powerful ML technique to offer us meaningful suggestions about the products that we may buy or like.

In this chapter, with a hands-on and practical approach, we'll execute the main implementation steps to build a new recommendation engine using the matrix factorization algorithm.

With a gradual and incremental approach and by leveraging BigQuery ML, we'll cover the following topics:

  • Introducing the business scenario
  • Discovering matrix factorization
  • Configuring BigQuery Flex Slots
  • Exploring and preparing the dataset
  • Training the matrix factorization model
  • Evaluating the matrix factorization model
  • ...